Uncertainty Prediction of Remaining Useful Life Using Long Short-Term Memory Network Based on Bootstrap Method

Liao, Yuan; Zhang, Linxuan; Liu, Chongdang

doi:10.1109/icphm.2018.8448804

Cited by 73 publications

(31 citation statements)

References 21 publications

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“…In this section, we apply functional MLP ('FMLP') to conduct RUL estimation task for a widely-used benchmark data set called NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) data [28]. We compare the performance of functional MLP with a variety of state-of-the-art deep learning approaches, including the Convolutional Neural Network model ('CNN') in [10], the Deep Weibull network ('DW-RNN') and the multi-task learning network ('MTL-RNN') in [6], the Long Short-Term Memory method ('LSTM') [3], and the bootstrapping based Long Short-Term Memory method ('LSTMBS') [11]. As shown by the experimental results, the proposed functional MLP approach significantly outperforms all these alternative methods.…”

Section: Experiments On C-mapss Data Setmentioning

confidence: 99%

Remaining Useful Life Estimation Using Functional Data Analysis

Wang

Zheng

Farahat

et al. 2019

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Remaining Useful Life (RUL) of an equipment or one of its components is defined as the time left until the equipment or component reaches its end of useful life. Accurate RUL estimation is exceptionally beneficial to Predictive Maintenance, and Prognostics and Health Management (PHM). Data driven approaches which leverage the power of algorithms for RUL estimation using sensor and operational time series data are gaining popularity. Existing algorithms, such as linear regression, Convolutional Neural Network (CNN), Hidden Markov Models (HMMs), and Long Short-Term Memory (LSTM), have their own limitations for the RUL estimation task. In this work, we propose a novel Functional Data Analysis (FDA) method called functional Multilayer Perceptron (functional MLP) for RUL estimation. Functional MLP treats time series data from multiple equipment as a sample of random continuous processes over time. FDA explicitly incorporates both the correlations within the same equipment and the random variations across different equipment's sensor time series into the model. FDA also has the benefit of allowing the relationship between RUL and sensor variables to vary over time. We implement functional MLP on the benchmark NASA C-MAPSS data and evaluate the performance using two popularly-used metrics. Results show the superiority of our algorithm over all the other state-of-the-art methods.

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Section: Experiments On C-mapss Data Setmentioning

confidence: 99%

Remaining Useful Life Estimation Using Functional Data Analysis

Wang

Zheng

Farahat

et al. 2019

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

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“…The proposed method was compared with other existing RUL estimation methods, which are HDNN [31], CNN-LSTM [32], The best result of each column is red and the second-best result is blue. [33], DCNN [15], BLSTM-ED [21], and LSTM-BS [34]. In table II, the proposed method achieved RMSE performance ratings that were 6.64%, 2.13%, and 0.33% higher than the other existing methods in the case of the FD001, FD003, and FD004 respectively.…”

Section: ) Comparison With State-of-the-arts Methodsmentioning

confidence: 94%

Attention-Based Bidirectional LSTM-CNN Model for Remaining Useful Life Estimation

Song

Park

Hong

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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In many industries, prognostic health management (PHM) technology has become important as a key technology to increase reliability and operational efficiency. Recently, several methods using a deep learning architecture to estimate the remaining useful life (RUL) as a part of the PHM have been presented. However, the limitation of existing methods is that they do not explicitly capture the relationship among different time sequences, which reduces the accuracy of RUL estimation. This paper proposes a novel RUL estimation algorithm using the attention mechanism to solve this problem. The proposed method applies scaled dot product attention to the encoder and the decoder consisting of long short-term memory, convolutional neural network and fully connected layer. The encoder applies self-attention to extract the association between time sequences, and the decoder extracts the association between the target RUL value and the time sequences using the representative vector of the RUL. Therefore, the proposed model has better performance to capture the long-term dependency in the sequence data and outperforms other state-of-the-art models in the experimental results. In addition, the extracted attention map shows that our model has better interpretability for RUL estimation.

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“…To handle this issue, Zheng et al (Zheng, Ristovski, Farahat, & Gupta, 2017) (Hsu & Jiang, 2018) proposed an LSTM to address the RUL prediction problem for turbine engines, which is able effectively to extract temporal dependencies from the historical data. Liao et al (Liao, Zhang, & Liu, 2018) have used LSTM relying on the bootstrap procedure for uncertainty estimation of RUL. The bootstrap method is a good solution to obtain uncertainty prediction without any sensor data distribution.…”

Section: Rnn and Its Variantsmentioning

confidence: 99%

RUL Estimation Enhancement using Hybrid Deep Learning Methods

Remadna

Terrissa

Ayad

et al. 2021

IJPHM

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The turbofan engine is one of the most critical aircraft components. Its failure may introduce unwanted downtime, expensive repair, and affect safety performance. Therefore, It is essential to accurately detect upcoming failures by predicting the future behavior health state of turbofan engines as well as its Remaining Useful Life. The use of deep learning techniques to estimate Remaining Useful Life has seen a growing interest over the last decade. However, hybrid deep learning methods have not been sufficiently explored yet by researchers.In this paper, we proposed two-hybrid methods combining Convolutional Auto-encoder (CAE), Bi-directional Gated Recurrent Unit (BDGRU), Bi-directional Long-Short Term Memory (BDLSTM), and Convolutional Neural Network (CNN) to enhance the RUL estimation. The results indicate that the hybrid methods exhibit the most reliable RUL prediction accuracy and significantly outperform the most robust predictions in the literature.

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Uncertainty Prediction of Remaining Useful Life Using Long Short-Term Memory Network Based on Bootstrap Method

Cited by 73 publications

References 21 publications

Remaining Useful Life Estimation Using Functional Data Analysis

Remaining Useful Life Estimation Using Functional Data Analysis

Attention-Based Bidirectional LSTM-CNN Model for Remaining Useful Life Estimation

RUL Estimation Enhancement using Hybrid Deep Learning Methods

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